- avoid constructing model_fn more than once when reading config
- keep inference package exports focused on public entry points
- rename extra strategy arguments to strategy_kwargs
- Eliminate core/ directory into cache/, runtime/, network/ subpackages plus flat modules
- Split cache.py (647 lines) into cache/{buffer,strategy,pool}.py by layer
- Add explicit ContiguousStrategy, make AllocationStrategy a real ABC
- Move TaskCacheState to cache/strategy.py, drop string forward references
- Rename api/ to network/, server.py to app.py
- Move sample.py into runtime/ alongside executor and graph
- Simplify TaskCacheManager.__init__ to single pool param
- Expose pool.strategy and pool.req_pool as public properties
- Fix KVCache import in attention_backend.py (TYPE_CHECKING guard)
- Fix steady-state decode reading uninitialized position_ids on first step
- Converts detached strategy metrics before returning loss output
- Removes redundant item conversion from the trainer loop
- Updates the documented contract and regression tests
- Propagates MoE load-balancing loss through model outputs
- Logs task, auxiliary, and weighted losses across strategies
- Computes only explicitly requested callback metrics
- Preserves tensor compute_loss API and adds regression tests
- add GradSNRTracker to metric_util.py computing SNR = E[g]^2 / Var(g) via per-parameter EMA moments
- add grad_snr_tracker field to TrainContext (instantiated by default)
- register grad_snr in MetricCallback, update tracker on each optimizer step before metrics are recorded
- add grad_snr to default --metrics in train.py CLI
- Add broadcast_state_dict to sync state_dict from rank-0 to all ranks
- Fix create_ref_model returning None on non-rank-0 under FSDP
- Fix sync_old_model only updating old_model on rank-0 under FSDP
- Split skip_no_cuda/skip_no_kernel markers and hoist to top-level conftest
- Add distributed tests for broadcast_state_dict and create_ref_model
- Extract _resolve_base_type and _validate_component as module-level helpers
- Replace ForwardRef._evaluate private API with eval in module namespace
- Remove broad except Exception in __init_subclass__, _component_base always set
- Replace direct _entries mutation in strategy.py with register() call form
- Remove dead TOKENIZER_CLASSES registry from AutoTokenizer
- Extract ModelFactory(BaseFactory[nn.Module]) as pure factory
- AutoModel now inherits only nn.Module, no factory state
- Move @AutoModel.register to @ModelFactory.register in transformer.py and encoder.py
- Move create_ref_model from astrai/parallel/utils.py into executor.py
- Remove unused ColumnParallelLinear/RowParallelLinear (module.py)
- Update imports in strategy.py and train_context.py
- Drop unused astrai.parallel.utils and astrai.parallel.module
- unshard() and full_tensor() are collective ops, all ranks must participate
- Old code returned None on non-rank-0 before calling unshard, causing deadlock
- Fix: all ranks unshard/full_tensor, only rank-0 keeps the result
- Move create_ref_model to parallel/utils.py, accept executor+model directly
- Guard create_ref_model and sync_old_model against None on non-rank-0
- Fix pre-existing ruff lint warnings (F401, F541, F841, E741)
- Exclude .md/.json/.yml from ruff format check
- Unblock SIGTERM/SIGINT via pthread_sigmask in early signal handler
- Do not restore SIG_DFL on unregister to prevent pending signal kills
- Register SIGTERM/SIGINT handlers in training loop, set stop flag on signal
- Check stop_requested at each epoch/batch boundary, break and call on_error to save checkpoint
- LocalStrategy parent forwards signal to child processes via terminate(), waits up to 600s for graceful exit
- TrainContext gains threading.Event-based stop_requested/request_stop
- Tests verify SIGTERM/SIGINT trigger checkpoint save with exit code 0, works on both CPU and GPU
- add loss_mask parameter to get_logprobs to decouple attention from loss masking
- DPO/GRPO strategies compose key-padding + causal mask before model forward
- prevents prompt tokens from being masked out of attention and missing causal masking
move inject_lora() before load_state_dict in _before_wrap so that
LoRA adapter weights from a checkpoint are properly restored on
training resume. Previously, inject happened after load, causing
lora_A/lora_B keys to be silently ignored (strict=False).
Co-Authored-By: ccx1324 <2424441089@qq.com>
- RolloutGenerator._instruction_to_messages builds system/user/assistant list (instruction->system, input->user, output->assistant), replacing single-user-turn concatenation
- Remove _iter_samples helper; _prepare_prompts zips parallel list-of-strings fields directly per the collate_fn contract
- Tests adopt a system-aware chat template and pin the three-field role mapping
- Drop unused imports caught by ruff F401 (torch.Tensor in scheduler.py, iter_raw_records in pipeline.py, Tuple in evaluate_rouge.py)
- train_context.py referenced non-existent cfg.batch_size, replaced with cfg.batch_per_device
- default group_size lowered from 8 to 1: without a group concept (DPO), scheduler batch equals batch_per_device; rollout-based DPO can opt in via extra_kwargs['group_size']>=2
- inline expressions (rollout_batch_size, max_seq_len) extracted for readability
- add tests/trainer/test_online_e2e.py: end-to-end online_dpo via Trainer.train, exercising KV-cache-backed rollout path
- RolloutGenerator now delegates prefill/decode to InferenceScheduler.run_batch (sync API, no background thread), sharing one KV-cache code path with the inference server and eliminating O(n^2) recompute in rollout
- Add sample(return_logprobs=) and Executor.execute_decode(return_logprobs=) to expose behaviour-policy log-probs through the engine; Task gains output_logprobs
- RolloutResult now subclasses RawRollout (adds rewards only), removing duplicated fields
- RolloutRunner.__call__ returns (result, is_fresh) instead of relying on object identity, removing the fragile refresh-detection contract
- Remove O(n^2) generate_responses helper and dead code (_tokenize_prompts, unused old_model arg)
- train_context.py wires InferenceScheduler directly instead of hand-rolling SamplingPipeline
- Tests: +11 covering return_logprobs, run_batch, and KV-cache-backed rollout semantics; 404 pass
- BaseExecutor.prepare now takes factories and instantiates model via model_fn(), runs before_wrap hook, wraps DDP/FSDP, then builds optimizer/scheduler on the wrapped model
- optimizer/scheduler creation moved into executor.prepare, eliminating the old 'create-then-wrap' hack reliance on use_orig_params=True
- FSDPExecutor/BaseExecutor accept **_extra kwargs to tolerate DDP-only keys (broadcast_buffers, gradient_as_bucket_view) being forwarded via executor_kwargs
- dataloader builds stay external; executor only handles model/optimizer/scheduler
- train_context.py rewritten to load checkpoint state_dict before prepare via a before_wrap closure
- Drop BaseStrategy.model_fn (stored but never read)
- Drop model_fn= passed to StrategyFactory.create in train_context
- Simplify FSDPExecutor.clip_grad_norm None branch to delegate to super()
- Remove DDPExecutor._gather_state_dict override (identical to base)
- DPO requires sequence-level sum of token logprobs, not per-token mean
- mean reduction made beta*ratio_diff ~0.03 (near-zero gradient)
- loss stalled at 0.6931 because logsigmoid(0.03) has vanishing grad
- sum gives beta*ratio_diff ~10 with meaningful gradients
- Store gains fetch_record/num_records alongside stream fetch/__len__
- save_bin/load_bin support per-record offsets via record_keys param
- H5Store/MmapStore/JsonlStore all support dual stream+record access
- DPODataset/GRPODataset use fetch_record, no cross-record concat
- dpo_collate_fn + collate_fn wired through TrainConfig
- fixes attention context leakage in DPO from windowed concatenation
- Add --resume bool flag to train.py CLI
- --param_path always loads weights only by default
- --resume restores epoch, consumed_samples, optimizer & scheduler
- Checkpoint.load() now preserves full meta dict
- Update test_early_stopping to use new param_path/resume API
- Mask prompt tokens to 0 so their logprobs excluded from ratio/KL
- Switch to token-level ratio + PPO clipping via reduction='none'
- Slice response token logprobs from full sequence output
- Replace k3 KL estimator with non-negative k1 estimator
- Fix epsilon from finifo.eps (~1e-38) to 1e-8
- Remove unused 'reduction' param from GRPOStrategy.__init__
- Clarify offline batch semantics in docstring
- Add 11 unit tests for masking, advantage, KL, sync, clipping
- Sync training.md and architecture.md docs
- Remove separate ValidationCallback, merge into MetricCallback
- Progress bar now tracks optimizer steps instead of micro-steps
- Remove unused log_interval config field and CLI flag
- Fix validation all_reduce: use SUM(loss, count) instead of AVG
- Simplify metric logging: always log every optimizer step
- Add grad_norm display to progress bar
ViperEkura
ccx1324andccx